Data Dictionary: Multi-source Company API
Find all data points with explanations available in the Multi-source Company API data.
Each category includes a table listing the available data points, their explanations, and data types.
Metadata
id
Company record identification key in our database
Integer
source_id
Identifier assigned by Professional Network
String
expired_domain
Indicates if the domain is expired
Boolean/integer
unique_domain
Indicates if the domain is unique
Boolean/integer
unique_website
Indicates if the website is unique
Boolean/integer
last_updated_at
Last update date of the record in the YYYY-MM-DD format
String (date)
created_at
Record creation date in the YYYY-MM-DD format
String (date)
See a snippet of the dataset for reference:
"id": 8369825,
"source_id": "9082300",
"expired_domain": 0,
"unique_domain": 1,
"unique_website": 1,
"last_updated_at": "2025-04-28",
"created_at": "2022-01-21"Firmographics
company_name
Company name
String
company_name_alias
All name variations associated with the company
String
company_legal_name
Legal company name
String
company_logo
Base64-encoded image data of the company's logo
String
company_logo_url
Logo URL (available from Professional network only)
String
is_b2b
Indicates if the company operates in a business-to-business model:
1 – b2b company
0 – b2c company
Integer
industry
Company's industry
String
type
Company type
String
founded_year
Founding year
String (date)
See a snippet of the dataset for reference:
"company_name": "Example Company",
"company_legal_name": "Example Company, Inc.",
"company_name_alias": [
"example-company.com",
"Example Company"
"Example Company, Inc. "
]
"company_logo": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK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\\r\\nHHqjQ28Rijjkusl2XBJK9Cea8gooAKKKKACiiigAooooAKKKKACiiigD/9k=",
"company_logo_url": "https://www.professional-network.com/in/logo-url",
"is_b2b": 1,
"industry": "Software Development",
"founded_year": "2000",SIC and NAICS codes
sic_codes
Company's SIC codes
Array of strings
naics_codes
Company's NAICS codes
Array of strings
See a snippet of the dataset for reference:
"sic_codes": [
"87",
"874"
],
"naics_codes": [
"32",
"325"
],Descriptions
description
Company description
String
description_enriched
Company description, enriched with LLM
String
description_metadata_raw
Company description (parsed from external sources not included in our firmographic data)
String
See a snippet of the dataset for reference:
"description": "Example Company (Nasdaq: EXMP) is a proven cloud CCaaS platform that helps business leaders redefine customer engagement and transform their contact center’s performance. Decision-makers use Example Company to improve customer experience, boost agent productivity, empower their managers, and enhance their system orchestration capabilities. Everything needed to deliver game-changing results can be seamlessly integrated and configured to maximize your success: Omnichannel Communications, AI, a Contact Center CRM, and Workforce Engagement Management tools. For more than 20 years, clients of all sizes and industries have trusted Example Company’s scalable and reliable cloud platform to power billions of omnichannel interactions every year.",
"description_enriched": "Example Company is a cloud-based call and contact center software provider that offers a range of products and solutions for businesses of all sizes. Their platform includes features such as voice, email, SMS, CRM, and workforce management, and they offer a variety of services to support their clients, including training, implementation, and consulting. ",
"description_metadata_raw": "Example Company CCaaS Ups Your Call / Contact Center Platform with Communication Software So You Can Be A Game-Changer: Voice, Email, SMS, CRM, WFM, for Inbound & Outbound Agents.",Company size
size_range
Company size based on employee count range (as selected by the company profile administrator)
String
employees_count
Number of employees on Professional Network who associated their experience with the company
Integer
See a snippet of the dataset for reference:
"size_range": "501-1000 employees",
"employees_count": 294,Inferred employee counts
employees_count_inferred
Estimated number of employees, calculated based on inferred employee data
Integer
employees_count_inferred_by_month
Estimated number of employees, calculated based on inferred employee data, for a three-year rolling window
Array of structs
employees_count_inferred
Estimated number of employees, calculated based on inferred employee data
Integer
date
Date identifier
String
See a snippet of the dataset for reference:
{
"employees_count_inferred": 20,
"employees_count_inferred_by_month": [
{
"employees_count_inferred": 20,
"date": "202504"
},
{
"employees_count_inferred": 18,
"date": "202503"
}
]
}Categories & keywords
categories_and_keywords
Categories and keywords assigned to the company profile and products across various platforms
Array of strings
See a snippet of the dataset for reference:
"categories_and_keywords": [
"call/contact center software provider",
"call & contact center software",
"contact center software"
],Ownership & status
status
Operational and ownership status
Array of objects (struct)
value
Current operational status
String
comment
Current ownership status
String
See a snippet of the dataset for reference:
"status": {
"value": "active",
"comment": "Acquired"
},ownership_status
Ownership status
String
parent_company_information
Parent company details
Object (struct)
parent_company_name
Parent company name
String
parent_company_website
Parent company website
String
date
Date of the information provided in MM/YYYY format
String (date)
See a snippet of the dataset for reference:
"ownership_status": "Public",
"parent_company_information": {
"parent_company_name": "Parent Company",
"parent_company_website": "https://www.parent-company.com/",
"date": "10/2023"
},Company updates
company_updates
Information from posts published by the company
Array of objects
followers
Profile follower count
Integer
date
Publish date
String
description
Published text
Note: may contain control characters
String
reactions_count
Number of reactions on the post
Integer
comments_count
Number of comments on the post
Integer
reshared_post_author
Reshared post author
String
reshared_post_author_url
Profile URL of the reshared post author
String
reshared_post_author_headline
Headline of the reshared post author
String
reshared_post_description
Reshared post text
String
reshared_post_followers
The number of followers of the reshared post author
Integer
reshared_post_date
Date the reshared post was published
String
See a snippet of the dataset for reference:
"company_updates": [
{
"followers": 1371,
"date": "2025-03-30",
"description": "Example description",
"reactions_count": 22,
"comments_count": 2,
"reshared_post_author": "John Doe",
"reshared_post_author_url": "https://www.professional-network.com/john-doe",
"reshared_post_author_headline": "Co-Founder at Example Company, TEDx & Keynote Speaker",
"reshared_post_description": "Example description",
"reshared_post_followers": 45,
"reshared_post_date": "1mo"
}
]Locations
hq_region
Region of the company's HQ location
Array of strings
hq_country
Country where the company's headquarters is located
String
hq_country_iso2
ISO 2-letter code of the headquarters country
String
hq_country_iso3
ISO 3-letter code of the headquarters country
String
hq_location
Headquarters location
String
hq_full_address
Full address of the headquarters
String
hq_city
Headquarters city
String
hq_state
Headquarters state
String
hq_street
Headquarters street address
String
hq_zipcode
Headquarters zip code
String
company_locations
List of company locations
Array of objects (structs)
location_address
Company location address
String
is_primary
Indicates if this is the primary company location
Boolean
See a snippet of the dataset for reference:
"hq_region": [
"Americas",
"Northern America",
"AMER"
],
"hq_country": "United States",
"hq_country_iso2": "US",
"hq_country_iso3": "USA",
"hq_location": "Austin, TX, United States",
"hq_full_address": "123 Main Street; Suite 500; Austin, TX 78701, US",
"hq_city": "Austin",
"hq_state": "Texas",
"hq_street": "123 Main Street; Suite 500",
"hq_zipcode": "78701",
"company_locations": [
{
"location_address": "123 Main Street; Suite 500; Austin, TX 78701, US",
"is_primary": true
}
],Public contact details
company_phone_numbers
Public phone numbers
Array of strings
company_emails
Public email addresses
Array of strings
See a snippet of the dataset for reference:
"company_phone_numbers": [
"(555) 123-4567"
],
"company_emails": [
"[email protected]"
],Follower counts & changes
Follower counts
followers_count_professional_network
Profile follower count on professional network
Integer
followers_count_twitter
Profile follower count on Twitter
Integer (long)
followers_count_owler
Profile follower count on Owler
Integer (long)
See a snippet of the dataset for reference:
"followers_count_professionnal_network": 12838,
"followers_count_twitter": 705,
"followers_count_owler": 188,Follower count changes
professional_network_followers_count_change
Changes in the number of followers over different periods on professional network
Object (struct)
current
Current number of followers on the professional network
Integer (long)
change_monthly
Monthly change in follower count on the professional network
Integer (long)
change_monthly_percentage
Monthly percentage change in follower count on the professional network
Float (double)
change_quarterly
Quarterly change in follower count on the professional network
Integer (long)
change_quarterly_percentage
Quarterly percentage change in follower count on the professional network
Float (double)
change_yearly
Yearly change in follower count on the professional network
Integer (long)
change_yearly_percentage
Yearly percentage change in follower count on the professional network
Float (double)
See a snippet of the dataset for reference:
"professional_network_followers_count_change": {
"current": 12779,
"change_monthly": 70,
"change_monthly_percentage": 0.5507907781886852,
"change_quarterly": 891,
"change_quarterly_percentage": 7.494952893674293,
"change_yearly": 1845,
"change_yearly_percentage": 16.873971099323214
},professional_network_followers_count_by_month
Professional network follower count changes by month
Array of objects (struct)
follower_count
Number of employees
Integer (long)
date
Record date
String (date)
See a snippet of the dataset for reference:
"professional_network_followers_count_by_month": [
{
"follower_count": 0,
"date": "2019-11"
},
{
"follower_count": 1,
"date": "2021-01"
}
],Competitors
competitors
Competitors and their similarity scores
Array of objects (struct)
company_name
Competitor's name
String
similarity_score
Score indicating the similarity to the record company
Integer (long)
competitors_websites
Details on the competitors' websites
Array of objects (struct)
website
Competitor's website URL
String
total_website_visits_monthly
Total number of monthly competitor's website visits
Integer (long)
category
Competitor's website category
String
rank_category
Competitor's website rank within its category
Integer
See a snippet of the dataset for reference:
"competitors": [
{
"company_name": "first competitor",
"similarity_score": 5321
},
{
"company_name": "second competitor",
"similarity_score": 5605
}
],
"competitors_websites": [
{
"website": "example-website.com",
"similarity_score": 100,
"total_website_visits_monthly": 91600,
"category": "Law and Government > Government",
"rank_category": 13758
},
{
"website": "example-website2.com",
"similarity_score": 100,
"total_website_visits_monthly": 403700,
"category": "Law and Government > Government",
"rank_category": 3510
}
],Product overview
pricing_available
Marks if service pricing information is available online
Boolean
free_trial_available
Marks if the company offers a free trial of their services
Boolean
demo_available
Marks if the company offers a demo
Boolean
is_downloadable
Marks if the company offers a downloadable file/service
Boolean
mobile_apps_exist
Marks if the company has mobile apps
Boolean
online_reviews_exist
Marks if the company has any online reviews
Boolean
documentation_exist
Marks if the company has public API docs
Boolean
See a snippet of the dataset for reference:
"pricing_available": false,
"free_trial_available": false,
"demo_available": false,
"is_downloadable": false,
"mobile_apps_exist": false,
"online_reviews_exist": false,
"documentation_exist": false,Product pricing
product_pricing_summary
Summary of product pricing plans
Array of objects (structs)
type
Pricing plan type
String
price
Plan price
String
details
Pricing plan details
String
See a snippet of the dataset for reference:
"product_pricing_summary": [
{
"type": "First plan",
"price": "38.00",
"details": "Per Month"
},
{
"type": "Second plan",
"price": "85",
"details": "per month (Annual Plan)"
}
],Product review scores
product_reviews_count
Total number of product reviews
Integer (long)
product_reviews_aggregate_score
Average score of product reviews
Float (double)
See a snippet of the dataset for reference:
"product_reviews_count": 74,
"product_reviews_aggregate_score": 4.513513513513513,product_reviews_score_by_month
Product review scores by month
Array of objects (structs)
product_reviews_score
Product review score
Float (double)
date
Record date
String (date)
See a snippet of the dataset for reference:
"product_reviews_score_by_month": [
{
"product_reviews_score": 4.4,
"date": "2019-11"
},
{
"product_reviews_score": 4.6,
"date": "2021-01"
}
],Product review score distribution
product_reviews_score_distribution
Distribution of product review scores
Object (struct)
score_1
Number of 1-star reviews
Integer (long)
score_2
Number of 2-star reviews
Integer (long)
score_3
Number of 3-star reviews
Integer (long)
score_4
Number of 4-star reviews
Integer (long)
score_5
Number of 5-star reviews
Integer (long)
See a snippet of the dataset for reference:
"product_reviews_score_distribution": {
"score_1": 0,
"score_2": 0,
"score_3": 4,
"score_4": 28,
"score_5": 42
},Product review score changes
product_reviews_score_change
Changes in the product review score over different periods
Object (struct)
current
Current product review score
Float (double)
change_monthly
Monthly change in product review score
Float (double)
change_quarterly
Quarterly change in product review score
Float (double)
change_yearly
Yearly change in product review score
Float (double)
See a snippet of the dataset for reference:
"product_reviews_score_change": {
"current": 4.3,
"change_monthly": 0.0,
"change_quarterly": 0.0,
"change_yearly": 0.0
},Financials
Annual revenue range
revenue_annual_range
Annual revenue range information from various sources
Object (struct)
source_4_annual_revenue_range,
source_6_annual_revenue_range
Revenue information from a specific source
Object (struct)
annual_revenue_range_from
Minimum annual revenue range
Float (double)
annual_revenue_range_to
Maximum annual revenue range
Float (double)
annual_revenue_range_currency
Revenue currency
String
See a snippet of the dataset for reference:
"revenue_annual_range": {
"source_4_annual_revenue_range": {
"annual_revenue_range_from": 1.0E8,
"annual_revenue_range_to": 5.0E8,
"annual_revenue_range_currency": "USD"
},
"source_6_annual_revenue_range": {
"annual_revenue_range_from": 1.0E8,
"annual_revenue_range_to": 2.0E8,
"annual_revenue_range_currency": "USD"
}
},Annual revenue
revenue_annual
Annual revenue information from various sources
Object (struct)
source_5_annual_revenue,
source_1_annual_revenue
Revenue information from a specific source
Object (struct)
annual_revenue
Annual revenue amount
Integer (long)
annual_revenue_currency
Revenue currency
String
See a snippet of the dataset for reference:
"revenue_annual": {
"source_5_annual_revenue": {
"annual_revenue": 143285000,
"annual_revenue_currency": "USD"
},
"source_1_annual_revenue": {
"annual_revenue": 1.36025E8,
"annual_revenue_currency": "USD"
}
},Quarterly revenue
revenue_quarterly
Quarterly revenue information
Object (struct)
value
Quarterly revenue amount
Float (double)
currency
Revenue currency
String
See a snippet of the dataset for reference:
"revenue_quarterly": {
"value": 3.5352E7,
"currency": "USD"
},IPO
is_public
Indicates if the company is publicly traded
Boolean
ipo_date
IPO date
String
ipo_share_price
Initial share price at the time of IPO. Value is present in USD only
Integer (long)
ipo_share_price_currency
Initial share price currency
String
See a snippet of the dataset for reference:
"is_public": 1,
"ipo_date": "2021-01-14",
"ipo_share_price": 10,
"ipo_share_price_currency": "USD",Stock information
stock_ticker
Company's stock ticker information
Array of objects (structs)
exchange
Stock exchange
String
ticker
Stock ticker
String
stock_information
Financial details of the company's stock
Array of objects (structs)
closing_price
Stock's closing price
Float (double)
currency
Stock currency
String
date
Date of the stock information in YYYY-MM-DD format
String (date)
marketcap
Market capitalization value
Float (double)
See a snippet of the dataset for reference:
"stock_ticker": [
{
"exchange": "NASDAQ",
"ticker": "AAPL"
}
]
"stock_information": [
{
"closing_price": 3.7300000190734863,
"currency": "USD",
"date": "2023-12-29",
"marketcap": 3.52990784E8
},
{
"closing_price": 3.680000066757202,
"currency": "USD",
"date": "2023-11-30",
"marketcap": 3.48259008E8
}
],Income statements
income_statements
Company's income statement details
Array of objects
cost_of_goods_sold
Total cost of goods sold by the company
Float (double)
cost_of_goods_sold_currency
Report currency
String
ebit
Earnings before interest and taxes
Float (double)
ebitda
Earnings before interest, taxes, depreciation, and amortization
Float (double)
ebitda_margin
EBITDA divided by total revenue
Float (double)
ebit_margin
EBIT divided by total revenue
Float (double)
earnings_per_share
Earnings per share
Float (double)
gross_profit
Profit after expenses related to manufacturing and selling its products or services
Float (double)
gross_profit_margin
Gross profit divided by revenue
Float (double)
income_tax_expense
Income tax expense
Float (double)
interest_expense
Total interest expense
Float (double)
interest_income
Interest income
Float (double)
net_income
Net income
Float (double)
period_display_end_date
Period end display date (e.g., fiscal year or quarter) based on how it's displayed in the source
String
period_end_date
Period end date in YYYY-MM-DD format
String (date)
period_type
Period type
String
pre_tax_profit
Profit before tax
Float (double)
revenue
Total revenue earned by the company
Float (double)
total_operating_expense
Total expenses related to operations
Float (double)
See a snippet of the dataset for reference:
"income_statements": [
{
"cost_of_goods_sold": 187884,
"currency": "USD",
"depreciation_mortization": 18561,
"ebit": 673028000,
"ebitda": 785395000,
"ebitda_margin": 0.23780319797984079,
"ebit_margin": 0.20378053174514263,
"earnings_per_share": -0.12,
"gross_profit": 145952,
"gross_profit_margin": 0.43719670736529315,
"income_tax_expense": 15625,
"interest_expense": 76,
"interest_income": 15920,
"period_display_end_date": "Q3, 2023",
"period_end_date": "2023-09-30",
"period_type": "q3",
"pre_tax_profit": 71516,
"revenue": 333836,
"revenue_growth": 0.10770647094038163,
"total_operating_expense": 2.7999E7
}
],Funding
Last funding round
last_funding_round_name
Last funding round name
String
last_funding_round_announced_date
Date when the last funding round was announced in YYYY-MM-DD format
String (date)
last_funding_round_lead_investors
Lead investors in the last funding round
Array of Strings
last_funding_round_amount_raised
Amount raised in the last funding round
Integer (long)
last_funding_round_amount_raised_currency
Funding round currency
String
last_funding_round_num_investors
Number of investors in the last funding round
Integer (long)
See a snippet of the dataset for reference:
"last_funding_round_name": "Venture Round - Example Company",
"last_funding_round_announced_date": "2014-03-25",
"last_funding_round_lead_investors": [
"John Doe Ventures"
],
"last_funding_round_amount_raised": 1234567890,
"last_funding_round_amount_raised_currency": "USD",
"last_funding_round_num_investors": 1,Funding rounds
funding_rounds
List of completed funding rounds
Array of objects (structs)
name
Funding round name
String
announced_date
Date when the funding round was announced in YYYY-MM-DD format
String (date)
lead_investors
Lead investors in the funding round
Array of Strings
amount_raised
Amount raised in the funding round
Integer (long)
amount_raised_currency
Funding round currency
String
num_investors
Number of investors in the funding round
Integer (long)
See a snippet of the dataset for reference:
"funding_rounds": [
{
"name": "Series E - Example Company",
"announced_date": "2007-06-19",
"lead_investors": [
"R&D Ventures"
],
"amount_raised": 7100000,
"amount_raised_currency": "USD",
"num_investors": 3
}
],Acquisitions
Acquired by
acquired_by_summary
Acquiring company details
Object (struct)
acquirer_name
Acquiring company name
String
announced_date
Acquisition date
String
price
Acquisition price
Integer (long)
currency
Acquisition currency
String
See a snippet of the dataset for reference:
"acquired_by_summary": {
"acquirer_name": "Parent Company",
"announced_date": "2023-10-04",
"price": 350000000,
"currency": "USD"
},Acquisitions
num_acquisitions_source_1,
num_acquisitions_source_2,
num_acquisitions_source_5
Number of completed company acquisitions based on information from various sources
Integer
acquisition_list_source_1,
acquisition_list_source_2,
acquisition_list_source_5
Company's acquisition information from various sources
Array of objects (struct)
acquiree_name
Acquired company name
String
announced_date
Date when the acquisition was announced in YYYY-MM-DD format
String (date)
price
Acquisition price
Integer
currency
Acquisition price currency
String
See a snippet of the dataset for reference:
"num_acquisitions_source_1": 2,
"acquisition_list_source_1": [
{
"acquiree_name": "First Acquiree",
"announced_date": "2019-12-10",
"price": 350000000,
"currency": "USD"
},
{
"acquiree_name": "Second Acquiree",
"announced_date": "2020-01-27",
"price": 350000000,
"currency": "USD"
}
],
"num_acquisitions_source_2": 2,
"acquisition_list_source_2": [
{
"acquiree_name": "First Acquiree",
"announced_date": "2020-01-27",
"price": 350000000,
"currency": "USD"
},
{
"acquiree_name": "Second Acquiree",
"announced_date": "2019-12-10",
"price": 350000000,
"currency": "USD"
}
],News features
num_news_articles
Number of news articles that mention the record company
Integer
news_articles
Details about the news articles featuring company updates
Array of objects (structs)
headline
News article headline
String
published_date
Date the article was published in YYYY-MM-DD format
String (date)
summary
News article summary
String
article_url
Full news article URL
String
source
Source of the news
String
See a snippet of the dataset for reference:
"num_news_articles": 1,
"news_articles": [
{
"headline": "Example Company Layoffs Hit Channel and Sales Team",
"published_date": "2024-01-08",
"summary": "Professional networking sites such as have seen the influx of Example Company employees posting about getting layoff notices in the past week. The cuts have come following the acquisition by contact-center-as-a-service (CCaaS) giant NICE.",
"article_url": "https://www.channelfutures.com/unified-communications/voicestream-technologies-inc-layoffs-hit-channel-and-sales-team",
"source": "News source example"
}
],Technographics
num_technologies_used
Number of technologies used by the company
Integer
technologies_used
List of technologies used by the company
Array of strings
technology
Technology name
String
first_verified_at
Date this technology was first assigned to the company
String (date)
last_verified_at
Date this technology was last assigned to the company
String (date)
See a snippet of the dataset for reference:
"num_technologies_used": 40,
"technologies_used": [
{
"technology": "React",
"first_verified_at": "2022-03-15",
"last_verified_at": "2024-10-15"
}
]Company websites and social media
website
Website URL
String
website_alias
All possible company website variations (collected from our firmographic sources)
String
professional_network_url
Professional network profile URL
String
twitter_url
Twitter profile URL
Array of strings
discord_url
Discord server URL
Array of strings
facebook_url
Facebook page URL
Array of strings
instagram_url
Instagram profile URL
Array of strings
pinterest_url
Pinterest profile URL
Array of strings
tiktok_url
TikTok profile URL
Array of strings
youtube_url
YouTube channel URL
Array of strings
github_url
GitHub profile URL
Array of strings
reddit_url
Reddit profile URL
Array of strings
financial_website_url
Financial network profile URL
String
See a snippet of the dataset for reference:
"website": "https://example-company.com",
"website_alias": [
"primarywebsite.org",
"primarywebsite.net",
"primary-site.com"
]
"professional_network_url": "https://www.professional-network.com/company/example-company",
"twitter_url": [
"https://twitter.com/example-company"
],
"discord_url": [
"https://discord.gg/example-company"
],
"facebook_url": [
"https://www.facebook.com/example-company"
],
"instagram_url": [
"https://www.instagram.com/example-company"
],
"pinterest_url": [
"https://www.pinterest.com/example-company"
],
"tiktok_url": [
"https://www.tiktok.com/@example-company"
],
"youtube_url": [
"https://www.youtube.com/example-company"
],
"github_url": [
"https://github.com/example-company"
],
"reddit_url": [
"https://www.reddit.com/user/example-company"
],
"financial_website_url": "https://www.financial-website.com/organization/example-company",Website traffic
Web traffic and topics
total_website_visits_monthly
Monthly website visits
Integer (long)
visits_change_monthly
Monthly change in website visits, shown in percentage
Float (double)
rank_global
Global rank of the website
Integer
rank_country
Country-specific rank of the website
Integer
rank_category
Category-specific rank of the website
Integer
bounce_rate
Percentage of visitors who leave the site after visiting one page
Float (double)
pages_per_visit
Average number of pages viewed per visit
Float (double)
average_visit_duration_seconds
Average duration of a visit in seconds
Float (double)
similarly_ranked_websites
Similarly ranked websites
Array of strings
top_topics
List of top topics associated with the website
Array of strings
See a snippet of the dataset for reference:
"total_website_visits_monthly": 72600,
"visits_change_monthly": 14.12,
"rank_global": 573826,
"rank_country": 119057,
"rank_category": 2160,
"bounce_rate": 41.26,
"pages_per_visit": 5.5,
"average_visit_duration_seconds": 287.0,
"similarly_ranked_websites": [
"datainsighttool.com",
"endsanalytics.io"],
"top_topics": [
"google",
"social network",
"social",
"social media",
"google apps"
],total_website_visits_change
Changes in the total number of website visits over different periods
Object (struct)
current
Current number of total website visits
Integer (long)
change_monthly
Monthly change in total website visits
Integer (long)
change_monthly_percentage
Monthly percentage change in total website visits
Float (double)
change_quarterly
Quarterly change in total website visits
Integer (long)
change_quarterly_percentage
Quarterly percentage change in total website visits
Float (double)
change_yearly
Yearly change in total website visits
Integer (long)
change_yearly_percentage
Yearly percentage change in total website visits
Float (double)
See a snippet of the dataset for reference:
"total_website_visits_change": {
"current": 15432,
"change_monthly": 89,
"change_monthly_percentage": 0.576321854392679,
"change_quarterly": 1043,
"change_quarterly_percentage": 6.781293846102947,
"change_yearly": 1983,
"change_yearly_percentage": 13.425986213489573
}total_website_visits_by_month
Website visits by month
Array of objects (structs)
total_website_visits
Website visits
Float (double)
date
Record date
String (date)
See a snippet of the dataset for reference:
"total_website_visits_by_month": [
{
"total_website_visits": 60,
"date": "2019-11"
},
{
"total_website_visits": 75,
"date": "2021-01"
}
],Visits by country
visits_breakdown_by_country
Breakdown of website visits by country
Array of objects (structs)
country
Visitor's country
String
percentage
Percentage of visits from one country
Float (double)
percentage_monthly_change
Monthly change in percentage of visits from one country
Float (double)
See a snippet of the dataset for reference:
"visits_breakdown_by_country": [
{
"country": "United States",
"percentage": 74.9,
"percentage_monthly_change": 31.74
}
],Visits by gender
visits_breakdown_by_gender
Breakdown of website visits by gender
Object (struct)
male_percentage
Percentage of visits by males
Float (double)
female_percentage
Percentage of visits by females
Float (double)
See a snippet of the dataset for reference:
"visits_breakdown_by_gender": {
"male_percentage": 64.04,
"female_percentage": 35.96
},Visits by age
visits_breakdown_by_age
Breakdown of website visits by age group
Object (struct)
age_18_24_percentage
Percentage of visits by users aged 18-24
Float
age_25_34_percentage
Percentage of visits by users aged 25-34
Float
age_35_44_percentage
Percentage of visits by users aged 35-44
Float
age_45_54_percentage
Percentage of visits by users aged 45-54
Float
age_55_64_percentage
Percentage of visits by users aged 55-64
Float
age_65_plus_percentage
Percentage of visits by users aged 65 and above
Float
See a snippet of the dataset for reference:
"visits_breakdown_by_age": {
"age_18_24_percentage": 22.92,
"age_25_34_percentage": 32.22,
"age_35_44_percentage": 15.47,
"age_45_54_percentage": 13.31,
"age_55_64_percentage": 10.3,
"age_65_plus_percentage": 5.78
},Employee review scores & changes
Review count
company_employee_reviews_count
Total number of employee reviews
Integer (long)
company_employee_reviews_aggregate_score
Average score of employee reviews
Float (double)
See a snippet of the dataset for reference:
"company_employee_reviews_count": 145,
"company_employee_reviews_aggregate_score": 4.1,Review score breakdown
employee_reviews_score_breakdown
Breakdown of employee review ratings by category
Object (struct)
business_outlook
Business outlook rating
Float (double)
career_opportunities
Career opportunities rating
Float (double)
ceo_approval
CEO approval rating
Float (double)
compensation_benefits
Compensation and benefits rating
Float (double)
culture_values
Culture and values rating
Float (double)
diversity_inclusion
Diversity and inclusion rating
Float (double)
recommend
Recommendation rating
Float (double)
senior_management
Senior management rating
Float (double)
work_life_balance
Work-life balance rating
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_breakdown": {
"business_outlook": 0.55,
"career_opportunities": 3.6,
"ceo_approval": 0.57,
"compensation_benefits": 4.2,
"culture_values": 4.1,
"diversity_inclusion": 3.7,
"recommend": 0.76,
"senior_management": 3.4,
"work_life_balance": 4.2
},Review score distribution
employee_reviews_score_distribution
Distribution of star ratings in the reviews
Object (struct)
score_1
Number of 1-star reviews
Integer (long)
score_2
Number of 2-star reviews
Integer (long)
score_3
Number of 3-star reviews
Integer (long)
score_4
Number of 4-star reviews
Integer (long)
score_5
Number of 5-star reviews
Integer (long)
See a snippet of the dataset for reference:
"employee_reviews_score_distribution": {
"score_1": 2,
"score_2": 7,
"score_3": 9,
"score_4": 14,
"score_5": 28
},Total rating change
employee_reviews_score_aggregated_change
Changes in the aggregated rating score of employee reviews over different periods
Object (struct)
current
Current aggregated score of employee reviews
Float (double)
change_monthly
Monthly change in the aggregated score
Float (double)
change_quarterly
Quarterly change in the aggregated score
Float (double)
change_yearly
Yearly change in the aggregated score
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_aggregated_change": {
"current": 4.3,
"change_monthly": 0.05,
"change_quarterly": 0.1,
"change_yearly": -0.2
}employee_reviews_score_aggregated_by_month
Aggregated review score by month
Array of objects (structs)
aggregated_score
Aggregated score
Float (double)
date
Record date
String (date)
See a snippet of the dataset for reference:
"employee_reviews_score_aggregated_by_month": [
{
"aggregated_score": 3.4,
"date": "2023-03"
},
{
"aggregated_score": 3.7,
"date": "2024-07"
}
],Rating change in the business outlook category
employee_reviews_score_business_outlook_change
Changes in the business outlook rating score
Object (struct)
current
Current business outlook score
Float (double)
change_monthly
Monthly change in the business outlook score
Float (double)
change_quarterly
Quarterly change in the business outlook score
Float (double)
change_yearly
Yearly change in the business outlook score
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_business_outlook_change": {
"current": 0.45,
"change_monthly": 0.02,
"change_quarterly": -0.05,
"change_yearly": 0.3
}employee_reviews_score_business_outlook_by_month
Business outlook score by month
Array of objects (structs)
business_outlook_score
Business outlook score
Float (double)
date
Record date
String (date)
See a snippet of the dataset for reference:
"employee_reviews_score_business_outlook_by_month": [
{
"business_outlook_score": 49.0,
"date": "2023-01"
},
{
"business_outlook_score": 49.0,
"date": "2022-09"
}
],Rating change in the career opportunities category
employee_reviews_score_career_opportunities_change
Changes in the career opportunities rating score from employee reviews
Object (struct)
current
Current career opportunities score
Float (double)
change_monthly
Monthly change in the career opportunities score
Float (double)
change_quarterly
Quarterly change in the career opportunities score
Float (double)
change_yearly
Yearly change in the career opportunities score
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_career_opportunities_change": {
"current": 3.8,
"change_monthly": -0.1,
"change_quarterly": -0.15,
"change_yearly": -0.3
}employee_reviews_score_career_opportunities_by_month
Career opportunities score by month
Array of objects (structs)
career_opportunities_score
Business outlook score
Float (double)
date
Record date
String (date)
See a snippet of the dataset for reference:
"employee_reviews_score_career_opportunities_by_month": [
{
"career_opportunities_score": 3.6,
"date": "2022-10"
},
{
"career_opportunities_score": 3.6,
"date": "2022-06"
}
],Rating change in the CEO approval category
employee_reviews_score_ceo_approval_change
Changes in the CEO approval rating score from employee reviews
Object (struct)
current
Current approval score of the CEO
Float (double)
change_monthly
Monthly change in the CEO approval score
Float (double)
change_quarterly
Quarterly change in the CEO approval score
Float (double)
change_yearly
Yearly change in the CEO approval score
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_ceo_approval_change": {
"current": 0.58,
"change_monthly": -0.03,
"change_quarterly": -0.05,
"change_yearly": -45.12
}employee_reviews_score_ceo_approval_by_month
CEO approval score by month
Array of objects (structs)
ceo_approval_score
CEO approval score
Float (double)
date
Record date
String (date)
See a snippet of the dataset for reference:
"employee_reviews_score_ceo_approval_by_month": [
{
"ceo_approval_score": 4.0,
"date": "2023-03"
},
{
"ceo_approval_score": 3.0,
"date": "2022-08"
}
],Rating change in the compensation and benefits category
employee_reviews_score_compensation_benefits_change
Changes in the compensation and benefits rating score from employee reviews
Object (struct)
current
Current compensation and benefits score
Float (double)
change_monthly
Monthly change in the compensation and benefits score
Float (double)
change_quarterly
Quarterly change in the compensation and benefits score
Float (double)
change_yearly
Yearly change in the compensation and benefits score
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_compensation_benefits_change": {
"current": 4.3,
"change_monthly": 0.05,
"change_quarterly": 0.07,
"change_yearly": -0.08
}employee_reviews_score_compensation_benefits_by_month
Compensation and benefits score by month
Array of objects (structs)
compensation_benefits_score
Compensation and benefits score
Float (double)
date
Record date
String (date)
See a snippet of the dataset for reference:
"employee_reviews_score_compensation_benefits_by_month": [
{
"compensation_benefits_score": 3.6,
"date": "2023-02"
},
{
"compensation_benefits_score": 3.6,
"date": "2022-12"
}
],Rating change in the culture and values category
employee_reviews_score_culture_values_change
Changes in the culture and values rating score from employee reviews
Object (struct)
current
Current culture and values score
Float (double)
change_monthly
Monthly change in the culture and values score
Float (double)
change_quarterly
Quarterly change in the culture and values score
Float (double)
change_yearly
Yearly change in the culture and values score
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_culture_values_change": {
"current": 4.1,
"change_monthly": 0.1,
"change_quarterly": 0.2,
"change_yearly": -0.3
}employee_reviews_score_culture_values_by_month
Culture and values score by month
Array of objects (structs)
culture_values_score
Culture and values score
Float (double)
date
Record date
String (date)
See a snippet of the dataset for reference:
"employee_reviews_score_culture_values_by_month": [
{
"culture_values_score": 3.9,
"date": "2023-03"
},
{
"culture_values_score": 3.9,
"date": "2022-11"
}
],Rating change in the culture and values category
employee_reviews_score_culture_values_change
Changes in the culture and values rating score from employee reviews
Object (struct)
current
Current culture and values score
Float (double)
change_monthly
Monthly change in the culture and values score
Float (double)
change_quarterly
Quarterly change in the culture and values score
Float (double)
change_yearly
Yearly change in the culture and values score
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_culture_values_change": {
"current": 4.1,
"change_monthly": 0.1,
"change_quarterly": 0.2,
"change_yearly": -0.3
}Rating change in the diversity and inclusion category
employee_reviews_score_diversity_inclusion_change
Changes in the diversity and inclusion rating score from employee reviews
Object (struct)
current
Current diversity and inclusion score
Float (double)
change_monthly
Monthly change in the diversity and inclusion score
Float (double)
change_quarterly
Quarterly change in the diversity and inclusion score
Float (double)
change_yearly
Yearly change in the diversity and inclusion score
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_diversity_inclusion_change": {
"current": 3.7,
"change_monthly": -0.1,
"change_quarterly": -0.2,
"change_yearly": -0.3
}employee_reviews_score_diversity_inclusion_by_month
Diversity and inclusion score by month
Array of objects (structs)
diversity_inclusion_score
Diversity and inclusion score
Float (double)
date
Record date
String (date)
"employee_reviews_score_diversity_inclusion_by_month": [
{
"diversity_inclusion_score": 4.5,
"date": "2023-03"
},
{
"diversity_inclusion_score": 4.5,
"date": "2022-11"
}
],Rating change in the recommendations category
employee_reviews_score_recommend_change
Changes in the recommendation rating score from employee reviews
Object (struct)
current
Current recommendation score
Float (double)
change_monthly
Monthly change in the recommendation score
Float (double)
change_quarterly
Quarterly change in the recommendation score
Float (double)
change_yearly
Yearly change in the recommendation score
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_recommend_change": {
"current": 0.76,
"change_monthly": -0.12,
"change_quarterly": -0.12,
"change_yearly": -0.24
}employee_reviews_score_recommend_by_month
Likelihood to recommend score by month
Array of objects (structs)
recommend_score
Likelihood to recommend score
Float (double)
date
Record date
String (date)
See a snippet of the dataset for reference:
"employee_reviews_score_recommend_by_month": [
{
"recommend_score": 0.54,
"date": "2024-07"
},
{
"recommend_score": 0.54,
"date": "2024-06"
}
],Rating change in the senior management category
employee_reviews_score_senior_management_change
Changes in the senior management rating score from employee reviews
Object (struct)
current
Current senior management score
Float (double)
change_monthly
Monthly change in the senior management score
Float (double)
change_quarterly
Quarterly change in the senior management score
Float (double)
change_yearly
Yearly change in the senior management score
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_senior_management_change": {
"current": 3.4,
"change_monthly": -0.1,
"change_quarterly": -0.1,
"change_yearly": -0.3
}employee_reviews_score_senior_management_by_month
Senior management score by month
Array of objects (structs)
senior_management_score
Senior management score
Float (double)
date
Record date
String (date)
See a snippet of the dataset for reference:
"employee_reviews_score_senior_management_by_month": [
{
"senior_management_score": 3.9,
"date": "2023-03"
},
{
"senior_management_score": 3.9,
"date": "2022-11"
}
],Rating change in the work and life balance category
employee_reviews_score_work_life_balance_change
Changes in the work-life balance rating score from employee reviews
Object (struct)
current
Current work-life balance score
Float (double)
change_monthly
Monthly change in the work-life balance score
Float (double)
change_quarterly
Quarterly change in the work-life balance score
Float (double)
change_yearly
Yearly change in the work-life balance score
Float (double)
See a snippet of the dataset for reference:
"employee_reviews_score_work_life_balance_change": {
"current": 4.2,
"change_monthly": 0.0,
"change_quarterly": 0.0,
"change_yearly": -0.1
}employee_reviews_score_work_life_balance_by_month
Work-life balance score by month
Array of objects (structs)
work_life_balance_score
Work-life balance score
Float (double)
date
Record date
String (date)
See a snippet of the dataset for reference:
"employee_reviews_score_work_life_balance_by_month": [
{
"work_life_balance_score": 3.8,
"date": "2023-01"
},
{
"work_life_balance_score": 3.8,
"date": "2022-09"
}
],Workforce trends
Key executives
key_executives
List of key executives
Array of objects (structs)
parent_id
Executive's identifier
String
member_full_name
Executive's name
String
member_position_title
Executive's job title
String
See a snippet of the dataset for reference:
"key_executives": [
{
"parent_id": 86953887,
"member_full_name": "John Doe",
"member_position_title": "Partner"
}
],Key employee change events
key_employee_change_events
List of key employee change events and corresponding information
Array of objects (structs)
employee_change_event_name
Employee change event
String
employee_change_event_date
Employee change event date in YYYY-MM-DD format
String (date)
employee_change_event_url
Event article URL
String
See a snippet of the dataset for reference:
"key_employee_change_events": [
{
"employee_change_event_name": "Example Company Appoints John Doe as Chief Investment Officer",
"employee_change_event_date": "2024-01-18",
"employee_change_event_url" : "https://www.vcaonline.com/news/2024011822/voicestream-technologies-appoints-john-doe-as-chief-investment-officer/"
}
],Key executive arrivals
key_executive_arrivals
List of new executives in the company.
Executives are considered members that have a decision_maker = true flag
Array of strings
parent_id
Executive's identifier
Integer (long)
member_full_name
Full name
String
member_position_title
Position title
String
arrival_date
Employment start date
String (date)
See a snippet of the dataset for reference:
"key_executive_arrivals": [
{
"parent_id": 423235614,
"member_full_name": "John Doe",
"member_position_title": "Partner",
"arrival_date": "Apr 2024"
},
{
"parent_id": 2241368,
"member_full_name": "Marry Moe",
"member_position_title": "Partner",
"arrival_date": "May 2024"
}
],Key executive departures
key_executive_departures
List of former executives in the company.
Executives are considered members that have a decision_maker = true flag
Array of strings
parent_id
Executive's identifier
Integer (long)
member_full_name
Full name
String
member_position_title
Position title
String
departure_date
Employment end date
String (date)
See a snippet of the dataset for reference:
"key_executive_departures": [
{
"parent_id": 692515608,
"member_full_name": "John Doe",
"member_position_title": "Partner",
"departure_date": "May 2024"
},
{
"parent_id": 83299323,
"member_full_name": "Danny Doe",
"member_position_title": "Partner",
"departure_date": "Aug 2024"
}
],Top companies
top_previous_companies
Top ten companies that likely were former workplaces for the current workforce. Likely to switch counts are based on member_experience data
Array of objects
company_id
Company identification key
Integer (long)
company_name
Company name
String
count
Count to identify the number of transitions
Integer (long)
top_next_companies
Top ten companies, people will likely switch after their current job.
Likely to switch counts are based on member_experience data
Array of objects
company_id
Company identification key
Integer (long)
company_name
Company name
String
count
Count to identify the number of transitions
Integer (long)
See a snippet of the dataset for reference:
"top_previous_companies": [
{
"company_id": 110,
"company_name": "Example Company",
"count": 5
}
],
"top_next_companies": [
{
"company_id": 110,
"company_name": "Example Sister Company",
"count": 3
}
]Employee count by department
employees_count_breakdown_by_department
Breakdown of employee count by department
Object (struct)
employees_count_medical
Number of employees in the medical department
Integer (long)
employees_count_sales
Number of employees in the sales department
Integer (long)
employees_count_hr
Number of employees in the HR department
Integer (long)
employees_count_legal
Number of employees in the legal department
Integer (long)
employees_count_marketing
Number of employees in the marketing department
Integer (long)
employees_count_finance
Number of employees in the finance department
Integer (long)
employees_count_techical
Number of employees in the tech department
Integer (long)
employees_count_consulting
Number of employees in the consulting department
Integer (long)
employees_count_operations
Number of employees in the operations department
Integer (long)
employees_count_general_management
Number of employees in the general management department
Integer (long)
employees_count_administrative
Number of employees in the administrative department
Integer (long)
employees_count_customer_service
Number of employees in the customer service department
Integer (long)
employees_count_project_management
Number of employees in the project management department
Integer (long)
employees_count_design
Number of employees in the design department
Integer (long)
employees_count_research
Number of employees in the research department
Integer (long)
employees_count_trades
Number of employees in the trades department
Integer (long)
employees_count_real_estate
Number of employees in the real estate department
Integer (long)
employees_count_education
Number of employees in the education department
Integer (long)
employees_count_other_department
Number of employees in other departments
Integer (long)
employees_count_product
Number of employees in the product department
Integer (long)
See a snippet of the dataset for reference:
"employees_count_breakdown_by_department": {
"employees_count_medical": 0,
"employees_count_sales": 24,
"employees_count_hr": 8,
"employees_count_legal": 2,
"employees_count_marketing": 5,
"employees_count_finance": 7,
"employees_count_tech": 63,
"employees_count_consulting": 2,
"employees_count_operations": 2,
"employees_count_other_department": 99,
"employees_count_product": 12
},employees_count_breakdown_by_department_by_month
Employee count changes by month and department
Object
employees_count_medical
Employee count in the medical department
Integer (long)
employees_count_sales
Employee count in the sales department
Integer (long)
employees_count_hr
Employee count in the HR department
Integer (long)
employees_count_legal
Employee count in the legal department
Integer (long)
employees_count_marketing
Employee count in the marketing department
Integer (long)
employees_count_finance
Employee count in the finance department
Integer (long)
employees_count_technical
Employee count in the technical department
Integer (long)
employees_count_consulting
Employee count in the consulting department
Integer (long)
employees_count_operations
Employee count in the operations department
Integer (long)
employees_count_product
Employee count in the product department
Integer (long)
employees_count_general_management
Employee count in the general management department
Integer (long)
employees_count_administrative
Employee count in the administrative department
Integer (long)
employees_count_customer_service
Employee count in the customer service department
Integer (long)
employees_count_project_management
Employee count in the project management department
Integer (long)
employees_count_design
Employee count in the design department
Integer (long)
employees_count_research
Employee count in the research department
Integer (long)
employees_count_trades
Employee count in the trades department
Integer (long)
employees_count_real_estate
Employee count in the real estate department
Integer (long)
employees_count_education
Employee count in the education department
Integer (long)
employees_count_other_department
Employee count in the other departments
Integer (long)
date
Record date.
Counts available from 2020.01
String (date)
See a snippet of the dataset for reference:
"employees_count_breakdown_by_department_by_month": [
{
"employees_count_breakdown_by_department": {
"employees_count_sales": 0,
"employees_count_hr": 0,
"employees_count_legal": 45,
"employees_count_marketing": 4,
"employees_count_finance": 0,
"employees_count_technical": 0,
"employees_count_consulting": 0,
"employees_count_operations": 55,
"employees_count_product": 0,
"employees_count_general_management": 0,
"employees_count_administrative": 0,
"employees_count_customer_service": 0,
"employees_count_project_management": 34,
"employees_count_design": 0,
"employees_count_research": 0,
"employees_count_trades": 56,
"employees_count_real_estate": 0,
"employees_count_education": 0,
"employees_count_other_department": 0,
"employees_count_other_department": 5
},
"date": "2021-01"
}
]employees_count_by_country
Employee count by country
Array of objects
country
Country
String
employee_count
Employee count
Integer
See a snippet of the dataset for reference:
"employees_count_by_country": [
{
"country": "Germany",
"employee_count": 1
},
{
"country": "Russia",
"employee_count": 1
}
],employees_count_by_country_by_month
Employee count by country and month
Array of objects (structs)
employees_count_by_country
Employee count by country
Object (struct)
country
Country
String
employee_count
Employee count
Integer (long)
date
Record date.
Counts available from 2020.01
String (date)
See a snippet of the dataset for reference:
"employees_count_by_country_by_month": [
{
"employees_count_by_country": [
{
"country": "United States",
"employee_count": 43
}
],
"date": "2021-01"
}
]employees_count_breakdown_by_region
Employee count breakdown by region
Object
employees_count_eastern_europe
Employee count in Eastern Europe
Integer
employees_count_latin_america
Employee count in Latin America
Integer
employees_count_southern_europe
Employee count in Southern Europe
Integer
employees_count_sub_saharan_africa
Employee count in Sub-Saharan Africa
Integer
employees_count_central_asia
Employee count in Central Asia
Integer
employees_count_northern_america
Employee count in Northern America
Integer
employees_count_australia_new_zealand
Employee count in Australia and New Zealand
Integer
employees_count_northern_europe
Employee count in Northern Europe
Integer
employees_count_south_eastern_asia
Employee count in Southeast Asia
Integer
employees_count_polynesia
Employee count in Polynesia
Integer
employees_count_southern_asia
Employee count in Southern Asia
Integer
employees_count_northern_africa
Employee count in Northern Africa
Integer
employees_count_melanesia
Employee count in Melanesia
Integer
employees_count_western_europe
Employee count in Western Europe
Integer
employees_count_western_asia
Employee count in Western Asia
Integer
employees_count_eastern_asia
Employee count in Eastern Asia
Integer
employees_count_micronesia
Employee count in Micronesia
Integer
employees_count_unknown
Employee count in unassigned region
Integer
See a snippet of the dataset for reference:
"employees_count_breakdown_by_region": {
"employees_count_eastern_europe" : 4,
"employees_count_latin_america": 5,
"employees_count_southern_europe": 6,
"employees_count_sub_saharan_africa": 0,
"employees_count_central_asia": 15,
"employees_count_northern_america": 0,
"employees_count_australia_new_zealand": 0,
"employees_count_northern_europe": 0,
"employees_count_south_eastern_asia": 0,
"employees_count_polynesia": 2,
"employees_count_southern_asia": 0,
"employees_count_northern_africa": 3,
"employees_count_melanesia": 0,
"employees_count_western_europe": 0,
"employees_count_western_asia": 0,
"employees_count_eastern_asia": 9,
"employees_count_micronesia": 0,
"employees_count_unknown": 0
}employees_count_breakdown_by_region_by_month
Employee count breakdown by region and date
Array of objects
employees_count_breakdown_by_region
Employee count breakdown by region
Object (struct)
employees_count_eastern_europe
Employee count in Eastern Europe
Integer
employees_count_latin_america
Employee count in Latin America
Integer
employees_count_southern_europe
Employee count in Southern Europe
Integer
employees_count_sub_saharan_africa
Employee count in Sub-Saharan Africa
Integer
employees_count_central_asia
Employee count in Central Asia
Integer
employees_count_northern_america
Employee count in Northern America
Integer
employees_count_australia_new_zealand
Employee count in Australia and New Zealand
Integer
employees_count_northern_europe
Employee count in Northern Europe
Integer
employees_count_south_eastern_asia
Employee count in Southeast Asia
Integer
employees_count_polynesia
Employee count in Polynesia
Integer
employees_count_southern_asia
Employee count in Southern Asia
Integer
employees_count_northern_africa
Employee count in Northern Africa
Integer
employees_count_melanesia
Employee count in Melanesia
Integer
employees_count_western_europe
Employee count in Western Europe
Integer
employees_count_western_asia
Employee count in Western Asia
Integer
employees_count_eastern_asia
Employee count in Eastern Asia
Integer
employees_count_micronesia
Employee count in Micronesia
Integer
employees_count_unknown
Employee count in unassigned region
Integer
date
Record date.
Counts available from 2020.01
String (date)
See a snippet of the dataset for reference:
"employees_count_breakdown_by_region_by_month": [
{
"employees_count_breakdown_by_region": {
"employees_count_eastern_europe": 3,
"employees_count_latin_america": 0,
"employees_count_southern_europe": 0,
"employees_count_sub_saharan_africa": 0,
"employees_count_central_asia": 0,
"employees_count_northern_america": 5,
"employees_count_australia_new_zealand": 0,
"employees_count_northern_europe": 7,
"employees_count_south_eastern_asia": 0,
"employees_count_polynesia": 0,
"employees_count_southern_asia": 0,
"employees_count_northern_africa": 0,
"employees_count_melanesia": 5,
"employees_count_western_europe": 0,
"employees_count_western_asia": 6,
"employees_count_eastern_asia": 0,
"employees_count_micronesia": 0,
"employees_count_unknown": 0
},
"date": "2021-01"
}
]Employee count by seniority
employees_count_breakdown_by_seniority
Breakdown of employee count by seniority level
Object (struct)
employees_count_owner
Number of employees with the Owner job title
Integer (long)
employees_count_founder
Number of employees with the Founder job title
Integer (long)
employees_count_clevel
Number of C-level employees
Integer (long)
employees_count_partner
Number of employees with the Partner job title
Integer (long)
employees_count_vp
Number of employees with the Vice President job title
Integer (long)
employees_count_head
Number of employees with the Head job title
Integer (long)
employees_count_director
Number of employees with the Director job title
Integer (long)
employees_count_manager
Number of employees with the Manager job title
Integer (long)
employees_count_senior
Number of senior-level employees
Integer (long)
employees_count_mid
Number of mid-level employees
Integer (long)
employees_count_junior
Number of junior-level employees
Integer (long)
employees_count_intern
Number of interns
Integer (long)
employees_count_specialist
Number of specialists
Integer (long)
employees_count_other_management
Number of employees in other management roles
Integer (long)
See a snippet of the dataset for reference:
"employees_count_breakdown_by_seniority": {
"employees_count_owner": 0,
"employees_count_founder": 0,
"employees_count_clevel": 3,
"employees_count_partner": 1,
"employees_count_vp": 8,
"employees_count_head": 1,
"employees_count_director": 10,
"employees_count_manager": 31,
"employees_count_senior": 73,
"employees_count_mid": 8,
"employees_count_junior": 1,
"employees_count_intern": 0,
"employees_count_specialist": 0,
"employees_count_other_management": 88
},employees_count_breakdown_by_seniority_by_month
Employee count breakdown seniority and date
Array of objects
employees_count_breakdown_by_seniority
Employee count breakdown by seniority
Object (struct)
employees_count_owner
Number of owners in the company
Integer
employees_count_founder
Number of founders in the company
Integer
employees_count_clevel
Number of C-level employees in the company
Integer
employees_count_partner
Number of partners in the company
Integer
employees_count_vp
Number of vice presidents in the company
Integer
employees_count_head
Number of *head-*level employees in the company
Integer
employees_count_director
Number of directors in the company
Integer
employees_count_manager
Number of managers in the company
Integer
employees_count_senior
Number of seniors in the company
Integer
employees_count_intern
Number of interns in the company
Integer
employees_count_specialist
Number of specialists in the company
Integer
employees_count_other_management
Number of other management employees in the company
Integer
date
Record date.
Counts available from 2020.01
Integer
See a snippet of the dataset for reference:
"employees_count_breakdown_by_seniority_by_month": [
{
"employees_count_breakdown_by_seniority": {
"employees_count_owner": 5,
"employees_count_founder": 3,
"employees_count_clevel": 7,
"employees_count_partner": 2,
"employees_count_vp": 6,
"employees_count_head": 4,
"employees_count_director": 10,
"employees_count_manager": 15,
"employees_count_senior": 20,
"employees_count_intern": 8,
"employees_count_specialist": 12,
"employees_count_other_management": 5
},
"date": "2023-09"
}
]Employee count changes
employees_count_change
Changes in the number of employees over different time periods
Object (struct)
current
Current number of employees
Integer (long)
change_monthly
Monthly change in employee count
Integer (long)
change_monthly_percentage
Monthly percentage change in employee count
Float (double)
change_quarterly
Quarterly change in employee count
Integer (long)
change_quarterly_percentage
Quarterly percentage change in employee count
Float (double)
change_yearly
Yearly change in employee count
Integer (long)
change_yearly_percentage
Yearly percentage change in employee count
Float (double)
See a snippet of the dataset for reference:
"employees_count_change": {
"current": 324,
"change_monthly": -26,
"change_monthly_percentage": -7.428571428571429,
"change_quarterly": -213,
"change_quarterly_percentage": -39.66480446927375,
"change_yearly": -244,
"change_yearly_percentage": -42.95774647887324
},employees_count_by_month
Employee count changes by month
Array of objects (structs)
employees_count
Number of employees
Integer (long)
date
Record date
String (date)
See a snippet of the dataset for reference:
"employees_count_by_month": [
{
"employees_count": 0,
"date": "2019-11"
},
{
"employees_count": 0,
"date": "2021-01"
}
],Active job postings
active_job_postings_count
Number of active job postings associated with the company
Integer (long)
active_job_postings
Active job postings
Array of structs
job_posting_id
Professional network Job ID
Long
job_posting_title
Job title posted by the recruiter
String
See a snippet of the dataset for reference:
"active_job_postings_count": 2,
"active_job_postings": [
{
"job_posting_id": 123456789123456,
"job_posting_title": "Product Manager"
}
]active_job_postings_count_by_month
Active job postings by month
Object (struct)
active_job_postings_count
Job posting count
Integer (long)
date
Record date.
Counts available from 2021.11
String (date)
See a snippet of the dataset for reference:
Active jobs count changes
active_job_postings_count_change
Changes in the number of active job postings over different periods
Object (struct)
current
Current number of active job postings
Integer (long)
change_monthly
Monthly change in active job postings count
Integer (long)
change_monthly_percentage
Monthly percentage change in active job postings count
Float (double)
change_quarterly
Quarterly change in active job postings count
Integer (long)
change_quarterly_percentage
Quarterly percentage change in active job postings count
Float (double)
change_yearly
Yearly change in active job postings count
Integer (long)
change_yearly_percentage
Yearly percentage change in active job postings count
Float (double)
See a snippet of the dataset for reference:
"active_job_postings_count_change": {
"current": 11540,
"change_monthly": 54,
"change_monthly_percentage": 0.467822984671254,
"change_quarterly": 743,
"change_quarterly_percentage": 6.431215746103567,
"change_yearly": 1594,
"change_yearly_percentage": 14.563924765213854Salaries
Base salary
base_salary
List of base salary details related to a specific job title
Array of objects (structs)
title
Job title
String
salary_p25
25th percentile salary
Float (double)
salary_median
Median salary
Float (double)
salary_p75
75th percentile salary
Float (double)
currency
Salary currency
String
pay_period
Pay period
String
salary_updated_at
Date when the salary information was last updated in YYYY-MMM-DD format
String (date)
See a snippet of the dataset for reference:
"base_salary": [
{
"title": "Software Engineer",
"salary_p25": 4500000.0,
"salary_median": 5500000.0,
"salary_p75": 7875000.0,
"currency": "COP",
"pay_period": "MONTHLY",
"salary_updated_at": "2018-05-31"
},
{
"title": "Devops Engineer",
"salary_p25": 1100000.0,
"salary_median": 1300000.0,
"salary_p75": 1500000.0,
"currency": "INR",
"pay_period": "ANNUAL",
"salary_updated_at": "2023-01-16"
}
],Additional pay
additional_pay
List of additional pay details related to a specific job title
Array of objects (structs)
title
Job title
String
additional_pay_values
Additional pay values
Array of objects (structs)
additional_pay_p25
25th percentile of additional pay
Float (double)
additional_pay_median
Median of additional pay
Float (double)
additional_pay_p75
75th percentile of additional pay
Float (double)
additional_pay_type
Additional pay type
String
currency
Pay currency
String
pay_period
Pay period
String
salary_updated_at
Date when the additional pay information was last updated in YYYY-MM-DD format
String (date)
See a snippet of the dataset for reference:
"additional_pay": [
{
"title": "Implementation Manager",
"additional_pay_values": [
{
"additional_pay_p25": 6598.52,
"additional_pay_median": 8798.02,
"additional_pay_p75": 12317.23,
"additional_pay_type": "Cash Bonus"
}
],
"currency": "USD",
"pay_period": "ANNUAL",
"salary_updated_at": "2024-02-10"
}
]Total salary
total_salary
List of total salary details related to a specific job title
Array of objects (structs)
title
Job title
String
salary_p25
25th percentile salary
Float (double)
salary_median
Median salary
Float (double)
salary_p75
75th percentile salary
Float (double)
currency
Salary currency
String
pay_period
Pay period
String
salary_updated_at
Date when the salary information was last updated in YYYY-MM-DD format
String (date)
See a snippet of the dataset for reference:
"total_salary": [
{
"title": "Marketing",
"salary_p25": 45.51,
"salary_median": 60.68,
"salary_p75": 84.07,
"currency": "USD",
"pay_period": "HOURLY",
"salary_updated_at": "2024-02-10"
}
],Last updated
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